Adaptive Linear Information Retrieval Models.

Peter Bollmann, S. K. Michael Wong: Adaptive Linear Information Retrieval Models. SIGIR 1987: 157-163

@inproceedings{DBLP:conf/sigir/BollmannW87,
  author    = {Peter Bollmann and
               S. K. Michael Wong},
  title     = {Adaptive Linear Information Retrieval Models},
  booktitle = {Proceedings of the Tenth Annual International ACM SIGIR Conference
               on Research and Development in Information Retrieval, New Orleans,
               Louisiana, USA, June 3-5, 1987},
  publisher = {ACM},
  year      = {1987},
  pages     = {157-163},
  ee        = {db/conf/sigir/BollmannW87.html},
  crossref  = {DBLP:conf/sigir/87},
  bibsource = {DBLP, http://dblp.uni-trier.de}
}

Abstract

Linear decision (retrieval) functions have been widely adopted in information retrieval systems such as in Boolean, vector, and probabilistic models. Based on measurement theory, adaptive linear retrieval models are proposed in this paper. A necessary and sufficient condition for the existence of a linear decision function is given. By ann inductive learning (feedback) process, techniques in linear integer programming can be directly applied to estimate parameters for automatic query formulation.

Copyright © 1987 by the ACM, Inc., used by permission. Permission to make digital or hard copies is granted provided that copies are not made or distributed for profit or direct commercial advantage, and that copies show this notice on the first page or initial screen of a display along with the full citation.

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Proceedings of the Tenth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, New Orleans, Louisiana, USA, June 3-5, 1987. ACM 1987
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